1 Getting Started

1.5 Some case studies

CASE STUDY 1: Paperware company

Problem: Want forecasts of each of hundreds of items. Series can be stationary, trended or seasonal. They currently have a large forecasting program written in-house but it doesn’t seem to produce sensible forecasts. They want me to fix it.

Additional information

  • Program written in COBOL making numerical calculations limited. It is not possible to do any optimisation.
  • They employ no statisticians and want the program to produce forecasts automatically.

CASE STUDY 1: Paperware company

Methods currently used

A
12 month average
C
6 month average
E
straight line regression over last 12 months
G
straight line regression over last 6 months
H
average slope between last year’s and this year’s values. (Equivalent to differencing at lag 12 and taking mean.)
I
Same as H except over 6 months.
K
I couldn’t understand the explanation.

CASE STUDY 2: PBS

CASE STUDY 2: PBS

The Pharmaceutical Benefits Scheme (PBS) is the Australian government drugs subsidy scheme.

  • Many drugs bought from pharmacies are subsidised to allow more equitable access to modern drugs.
  • The cost to government is determined by the number and types of drugs purchased. Currently nearly 1% of GDP.
  • The total cost is budgeted based on forecasts of drug usage.

CASE STUDY 2: PBS

CASE STUDY 2: PBS

  • In 2001: $4.5 billion budget, under-forecasted by $800 million.
  • Thousands of products. Seasonal demand.
  • Subject to covert marketing, volatile products, uncontrollable expenditure.
  • Although monthly data available for 10 years, data are aggregated to annual values, and only the first three years are used in estimating the forecasts.
  • All forecasts being done with the function in MS-Excel!

CASE STUDY 3: Car fleet company

Client: One of Australia’s largest car fleet companies

Problem: how to forecast resale value of vehicles? How should this affect leasing and sales policies?

Additional information

  • They can provide a large amount of data on previous vehicles and their eventual resale values.
  • The resale values are currently estimated by a group of specialists. They see me as a threat and do not cooperate.

CASE STUDY 4: Airline

CASE STUDY 4: Airline

CASE STUDY 4: Airline

Problem: how to forecast passenger traffic on major routes?

Additional information

  • They can provide a large amount of data on previous routes.
  • Traffic is affected by school holidays, special events such as the Grand Prix, advertising campaigns, competition behaviour, etc.
  • They have a highly capable team of people who are able to do most of the computing.